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一种基于压缩感知的信道估计算法 被引量：3

A Channel Estimation Technique Using CompressedSensing in LTE Systems
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摘要 信道估计是无线通信系统的关键技术之一。文章提出一种基于压缩感知(Compressed Sensing)的信道估计方法,这种方法通过分析信道时延和多普勒域的稀疏度,可以有效降低导频的数量,从而提高系统的频谱利用率。通过与最小均方(Least Square,LS)信道估计方法的对比,可以明显看到导频数量的降低。 Channel estimation is one key technology for wireless communication systems.A new channel estimation technique using compressed sensing（CS） is proposed.CS-based channel estimation exploits a channel＇s delay and Doppler sparseness to reduce the number of pilot signals,and then increase spectral efficiency.Simulation results demonstrate a significant reduction of the number of pilot signals relative to least-square channel estimation.
作者 焦东立 朱立东
机构地区 电子科技大学通信抗干扰技术重点实验室
出处 《空间电子技术》 2011年第3期16-19,共4页 Space Electronic Technology
基金 部级基金资助项目(9140A220309090C0201)
关键词 LTE OFDM 信道估计 压缩感知 稀疏重构 LTE OFDM Channel estimation Compressed sensing Sparse reconstruction
分类号 TN948.14 [电子电信—信号与信息处理]
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参考文献6

  • 1Cand E. J , Tao T. Near optimal signal recovery from random projections : Universial encoding strtegies [ J ]. IEEE Trans. Info. Thoey. 2006,52 (12) :5406 - 5425.
  • 2Zou J,Glbert A C,Straussm J,et al. Theoretical and experimental analysis of a randomized algorithm for sparse Fourier transform analysis [ J ] , Journal of Computational Physics,2006,211 (2) : 572 - 595.
  • 3Baraniuk R. A lecture on compressive sensing [ J ]. IEEE Siganl Processing Magazine, 2007,24(4) :118 -121.
  • 4Nowakrd F M, Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems [ J ]. IEEE J-STSP, 2007, 1 ( 4 ) : 586 - 598.
  • 5Bello P A, Characterization of randomly time-varient linear channels [ J ]. IEEE Trans. on Comm. Syst, 963,11:360 - 393.
  • 63GPP TS 36. 211 V 8.4.0. Physical Channel and Modulation. 2008,57 - 64.

同被引文献29

  • 1王东明,高西奇,尤肖虎,韩冰.宽带MIMO-OFDM系统信道估计算法研究[J].电子学报,2005,33(7):1254-1257. 被引量：21
  • 2CANDIS E J, ROMBERG J. Quantitative robust un- certainty principles and optimally sparse decomposi- tions [ J ]. Foundations of Compute Math, 2006,6(2): 227-254.
  • 3CANDS E J, WAKIN M B. An introduction to compres- sive sampling[J]. Signal Processing Magazine, IEEE, 2008, 25(2): 21-30.
  • 4BARANIUK R G. Compressive sensing[ J]. IEEE signal processing magazine, 2007, 24(4) : 118-121.
  • 5WAKIN M B, LASKA J N, DUARTE M F, et al. An ar- chitecture for compressive imaging [ C ]. Image Process- ing, 2006 IEEE International Conference on. IEEE, 2006 : 1273-1276.
  • 6CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incom- plete frequency information [ J ]. Information Theory, IEEE Transactions on, 2006, 52 (2) : 489-509.
  • 7FIQUEIREDO M A T, NOWAK R D, WRIGHT S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems [ J ]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4) : 586-597.
  • 8DO BA K, INDYK P, PRICE E, et al. Lower Bounds for Sparse Recovery[C]. SODA. 2010, 10: 1190-1197.
  • 9CANDES E J, TAO T. Near-optimal signal recovery from random projections : Universal encoding strategies? [ J]. Information Theory, IEEE Transactions on, 2006, 52 (12) : 5406-5425.
  • 10Ghassemi A, Ghasemnezad H, Gulliver T A. Compressive sens- ing based estimation of OFDM nonlinear distortion [ C ]//Proc of 2014 IEEE international conference on communication. [ s.1. ] : IEEE ,2014:5055-5059.

引证文献3

二级引证文献19

空间电子技术
2011年 第3期

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